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59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
# This work is licensed under the MIT license.
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# Copyright (c) 2013-2024 OpenMV LLC. All rights reserved.
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# https://github.com/openmv/openmv/blob/master/LICENSE
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#
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# TensorFlow Lite Object Detection Example
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#
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# This examples uses the builtin FOMO model to detect faces.
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import sensor
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import time
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import ml
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from ml.postprocessing import fomo_postprocess
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import math
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sensor.reset() # Reset and initialize the sensor.
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sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
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sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
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sensor.set_windowing((240, 240)) # Set 240x240 window.
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sensor.skip_frames(time=2000) # Let the camera adjust.
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# Load built-in FOMO face detection model
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model = ml.Model("/rom/fomo_face_detection.tflite")
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print(model)
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# Alternatively, models can be loaded from the filesystem storage.
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# model = ml.Model('<object_detection_modelwork>.tflite', load_to_fb=True)
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# labels = [line.rstrip('\n') for line in open("labels.txt")]
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colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
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(255, 0, 0),
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(0, 255, 0),
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(255, 255, 0),
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(0, 0, 255),
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(255, 0, 255),
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(0, 255, 255),
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(255, 255, 255),
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]
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clock = time.clock()
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while True:
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clock.tick()
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img = sensor.snapshot()
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for i, detection_list in enumerate(model.predict([img], callback=fomo_postprocess())):
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if i == 0:
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continue # background class
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if len(detection_list) == 0:
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continue # no detections for this class?
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print("********** %s **********" % model.labels[i])
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for (x, y, w, h), score in detection_list:
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center_x = math.floor(x + (w / 2))
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center_y = math.floor(y + (h / 2))
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print(f"x {center_x}\ty {center_y}\tscore {score}")
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img.draw_circle((center_x, center_y, 12), color=colors[i])
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print(clock.fps(), "fps", end="\n")
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